Overview

Dataset statistics

Number of variables16
Number of observations1780
Missing cells8883
Missing cells (%)31.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory212.2 KiB
Average record size in memory122.1 B

Variable types

NUM13
CAT2
BOOL1

Warnings

country has a high cardinality: 178 distinct values High cardinality
slaughtered_pigs_hd is highly correlated with production_pigs_tonnes and 1 other fieldsHigh correlation
production_pigs_tonnes is highly correlated with slaughtered_pigs_hd and 1 other fieldsHigh correlation
stocks_pigs_hd is highly correlated with production_pigs_tonnes and 1 other fieldsHigh correlation
producerprice_pigs_live_lcupertonne is highly correlated with producerprice_pigs_carcass_lcupertonne and 2 other fieldsHigh correlation
producerprice_pigs_carcass_lcupertonne is highly correlated with producerprice_pigs_live_lcupertonne and 2 other fieldsHigh correlation
producerprice_pigs_carcass_slcpertonne is highly correlated with producerprice_pigs_carcass_lcupertonne and 2 other fieldsHigh correlation
producerprice_pigs_live_slcpertonne is highly correlated with producerprice_pigs_carcass_lcupertonne and 2 other fieldsHigh correlation
yield_pigs_hgperhd has 40 (2.2%) missing values Missing
production_pigs_tonnes has 29 (1.6%) missing values Missing
slaughtered_pigs_hd has 20 (1.1%) missing values Missing
stocks_pigs_hd has 40 (2.2%) missing values Missing
producerprice_pigs_carcass_lcupertonne has 1373 (77.1%) missing values Missing
producerprice_pigs_live_lcupertonne has 1136 (63.8%) missing values Missing
producerprice_pigs_carcass_slcpertonne has 1373 (77.1%) missing values Missing
producerprice_pigs_live_slcpertonne has 1136 (63.8%) missing values Missing
producerprice_pigs_carcass_usdpertonne has 1373 (77.1%) missing values Missing
producerprice_pigs_live_usdpertonne has 1143 (64.2%) missing values Missing
producerprice_pigs_carcass_index has 619 (34.8%) missing values Missing
producerprice_pigs_live_index has 601 (33.8%) missing values Missing
country is uniformly distributed Uniform

Reproduction

Analysis started2022-04-08 16:39:18.206035
Analysis finished2022-04-08 16:39:48.156390
Duration29.95 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

country
Categorical

HIGH CARDINALITY
UNIFORM

Distinct178
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
Poland
 
10
Kenya
 
10
United Kingdom of Great Britain and Northern Ireland
 
10
Botswana
 
10
United States of America
 
10
Other values (173)
1730 
ValueCountFrequency (%) 
Poland100.6%
 
Kenya100.6%
 
United Kingdom of Great Britain and Northern Ireland100.6%
 
Botswana100.6%
 
United States of America100.6%
 
Azerbaijan100.6%
 
New Caledonia100.6%
 
Cook Islands100.6%
 
Lao People's Democratic Republic100.6%
 
China, Taiwan Province of100.6%
 
Other values (168)168094.4%
 
2022-04-08T09:39:48.272300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-08T09:39:48.457237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length52
Median length8
Mean length10.34269663
Min length4

year
Real number (ℝ≥0)

Distinct10
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.5
Minimum2011
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size13.9 KiB
2022-04-08T09:39:48.604428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12013
median2015.5
Q32018
95-th percentile2020
Maximum2020
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.873088484
Coefficient of variation (CV)0.001425496643
Kurtosis-1.224309899
Mean2015.5
Median Absolute Deviation (MAD)2.5
Skewness0
Sum3587590
Variance8.254637437
MonotocityNot monotonic
2022-04-08T09:39:48.720332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
201117810.0%
 
201217810.0%
 
201317810.0%
 
201417810.0%
 
201517810.0%
 
201617810.0%
 
201717810.0%
 
201817810.0%
 
201917810.0%
 
202017810.0%
 
ValueCountFrequency (%) 
201117810.0%
 
201217810.0%
 
201317810.0%
 
201417810.0%
 
201517810.0%
 
ValueCountFrequency (%) 
202017810.0%
 
201917810.0%
 
201817810.0%
 
201717810.0%
 
201617810.0%
 

yield_pigs_hgperhd
Real number (ℝ≥0)

MISSING

Distinct623
Distinct (%)35.8%
Missing40
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean655.6821839
Minimum155
Maximum1652
Zeros0
Zeros (%)0.0%
Memory size13.9 KiB
2022-04-08T09:39:48.874041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum155
5-th percentile300
Q1450
median650
Q3823.25
95-th percentile1016.15
Maximum1652
Range1497
Interquartile range (IQR)373.25

Descriptive statistics

Standard deviation267.374801
Coefficient of variation (CV)0.4077810982
Kurtosis1.913400083
Mean655.6821839
Median Absolute Deviation (MAD)184
Skewness0.9748975742
Sum1140887
Variance71489.28421
MonotocityNot monotonic
2022-04-08T09:39:49.074561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
500744.2%
 
400693.9%
 
300663.7%
 
450372.1%
 
600311.7%
 
550251.4%
 
1650201.1%
 
700191.1%
 
350181.0%
 
740160.9%
 
Other values (613)136576.7%
 
(Missing)402.2%
 
ValueCountFrequency (%) 
15510.1%
 
17110.1%
 
17510.1%
 
19010.1%
 
19920.1%
 
ValueCountFrequency (%) 
165220.1%
 
165110.1%
 
1650201.1%
 
164940.2%
 
164710.1%
 

production_pigs_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1609
Distinct (%)91.9%
Missing29
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean959594.7938
Minimum0
Maximum57661871
Zeros11
Zeros (%)0.6%
Memory size13.9 KiB
2022-04-08T09:39:49.259590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile114.5
Q11922.5
median18709
Q3217490
95-th percentile2155052.5
Maximum57661871
Range57661871
Interquartile range (IQR)215567.5

Descriptive statistics

Standard deviation5667773.922
Coefficient of variation (CV)5.90642421
Kurtosis79.09455491
Mean959594.7938
Median Absolute Deviation (MAD)18510
Skewness8.824259394
Sum1680250484
Variance3.212366123e+13
MonotocityNot monotonic
2022-04-08T09:39:49.428760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0110.6%
 
1990.5%
 
6650.3%
 
42050.3%
 
6740.2%
 
7640.2%
 
12000040.2%
 
7540.2%
 
11740.2%
 
55540.2%
 
Other values (1599)169795.3%
 
(Missing)291.6%
 
ValueCountFrequency (%) 
0110.6%
 
1990.5%
 
2010.1%
 
3110.1%
 
3210.1%
 
ValueCountFrequency (%) 
5766187110.1%
 
5741574010.1%
 
5671390010.1%
 
5645400010.1%
 
5591731910.1%
 

slaughtered_pigs_hd
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1672
Distinct (%)95.0%
Missing20
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean12217652.85
Minimum0
Maximum744917877
Zeros17
Zeros (%)1.0%
Memory size13.9 KiB
2022-04-08T09:39:49.629362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2499.5
Q133170.5
median355303.5
Q32622370.5
95-th percentile25007783
Maximum744917877
Range744917877
Interquartile range (IQR)2589200

Descriptive statistics

Standard deviation74272196.96
Coefficient of variation (CV)6.07908883
Kurtosis80.15663181
Mean12217652.85
Median Absolute Deviation (MAD)350071
Skewness8.934247075
Sum2.150306901e+10
Variance5.516359241e+15
MonotocityNot monotonic
2022-04-08T09:39:49.792136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0171.0%
 
1850050.3%
 
64850.3%
 
1400040.2%
 
250040.2%
 
150000040.2%
 
36000030.2%
 
55000030.2%
 
1300030.2%
 
2500030.2%
 
Other values (1662)170996.0%
 
(Missing)201.1%
 
ValueCountFrequency (%) 
0171.0%
 
7610.1%
 
8510.1%
 
8710.1%
 
64510.1%
 
ValueCountFrequency (%) 
74491787710.1%
 
73510400010.1%
 
73407718310.1%
 
72603983810.1%
 
72415600010.1%
 

stocks_pigs_hd
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1611
Distinct (%)92.6%
Missing40
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean8127653.168
Minimum10
Maximum486742946
Zeros0
Zeros (%)0.0%
Memory size13.9 KiB
2022-04-08T09:39:49.977214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile3579.05
Q137046.25
median433659.5
Q32443146
95-th percentile17210648.55
Maximum486742946
Range486742936
Interquartile range (IQR)2406099.75

Descriptive statistics

Standard deviation48092039.58
Coefficient of variation (CV)5.917088068
Kurtosis81.40257339
Mean8127653.168
Median Absolute Deviation (MAD)427923
Skewness8.990088586
Sum1.414211651e+10
Variance2.312844271e+15
MonotocityNot monotonic
2022-04-08T09:39:50.162074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5000160.9%
 
1000100.6%
 
300090.5%
 
1160.3%
 
550050.3%
 
3500050.3%
 
3000050.3%
 
5000040.2%
 
3220040.2%
 
800040.2%
 
Other values (1601)167293.9%
 
(Missing)402.2%
 
ValueCountFrequency (%) 
1010.1%
 
1160.3%
 
1210.1%
 
1310.1%
 
1410.1%
 
ValueCountFrequency (%) 
48674294610.1%
 
48511211710.1%
 
48491463710.1%
 
48030240010.1%
 
47893140020.1%
 
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
False
1650 
True
 
130
ValueCountFrequency (%) 
False165092.7%
 
True1307.3%
 
2022-04-08T09:39:50.308963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

producerprice_pigs_carcass_lcupertonne
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct377
Distinct (%)92.6%
Missing1373
Missing (%)77.1%
Infinite0
Infinite (%)0.0%
Mean2030616.16
Minimum1139
Maximum46711000
Zeros0
Zeros (%)0.0%
Memory size13.9 KiB
2022-04-08T09:39:50.447054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1139
5-th percentile1404.6
Q12074.5
median17621
Q3183015
95-th percentile8048333.3
Maximum46711000
Range46709861
Interquartile range (IQR)180940.5

Descriptive statistics

Standard deviation7894335.045
Coefficient of variation (CV)3.88765499
Kurtosis21.40139092
Mean2030616.16
Median Absolute Deviation (MAD)16121
Skewness4.687553628
Sum826460777
Variance6.23205258e+13
MonotocityNot monotonic
2022-04-08T09:39:50.632038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1762140.2%
 
648040.2%
 
1763730.2%
 
43540030.2%
 
275000030.2%
 
152030.2%
 
47603020.1%
 
450020.1%
 
950020.1%
 
158420.1%
 
Other values (367)37921.3%
 
(Missing)137377.1%
 
ValueCountFrequency (%) 
113910.1%
 
114010.1%
 
114410.1%
 
123210.1%
 
124510.1%
 
ValueCountFrequency (%) 
4671100010.1%
 
4639000010.1%
 
4626300010.1%
 
4617000010.1%
 
4607563410.1%
 

producerprice_pigs_live_lcupertonne
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct604
Distinct (%)93.8%
Missing1136
Missing (%)63.8%
Infinite0
Infinite (%)0.0%
Mean985987.0342
Minimum829
Maximum34651034
Zeros0
Zeros (%)0.0%
Memory size13.9 KiB
2022-04-08T09:39:50.810396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum829
5-th percentile1104.45
Q12577.5
median14643.5
Q3132615.25
95-th percentile3552622.95
Maximum34651034
Range34650205
Interquartile range (IQR)130037.75

Descriptive statistics

Standard deviation4374364.699
Coefficient of variation (CV)4.436533694
Kurtosis34.19726852
Mean985987.0342
Median Absolute Deviation (MAD)13430.5
Skewness5.814714403
Sum634975650
Variance1.913506652e+13
MonotocityNot monotonic
2022-04-08T09:39:50.979760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13228120.7%
 
680170.4%
 
504040.2%
 
1100040.2%
 
192857130.2%
 
2175330.2%
 
121230.2%
 
103420.1%
 
142420.1%
 
641820.1%
 
Other values (594)60233.8%
 
(Missing)113663.8%
 
ValueCountFrequency (%) 
82910.1%
 
89310.1%
 
91210.1%
 
93610.1%
 
95710.1%
 
ValueCountFrequency (%) 
3465103410.1%
 
3268891610.1%
 
3152979310.1%
 
3075738620.1%
 
3010000010.1%
 

producerprice_pigs_carcass_slcpertonne
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct377
Distinct (%)92.6%
Missing1373
Missing (%)77.1%
Infinite0
Infinite (%)0.0%
Mean1907944.464
Minimum1139
Maximum46711000
Zeros0
Zeros (%)0.0%
Memory size13.9 KiB
2022-04-08T09:39:51.186789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1139
5-th percentile1404.6
Q12074.5
median17621
Q3176108.5
95-th percentile6479928.8
Maximum46711000
Range46709861
Interquartile range (IQR)174034

Descriptive statistics

Standard deviation7728339.622
Coefficient of variation (CV)4.050610365
Kurtosis23.49112978
Mean1907944.464
Median Absolute Deviation (MAD)16113
Skewness4.913892725
Sum776533397
Variance5.972723332e+13
MonotocityNot monotonic
2022-04-08T09:39:51.349701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1762140.2%
 
648040.2%
 
1763730.2%
 
275000030.2%
 
152030.2%
 
43540030.2%
 
260020.1%
 
950020.1%
 
1543220.1%
 
158420.1%
 
Other values (367)37921.3%
 
(Missing)137377.1%
 
ValueCountFrequency (%) 
113910.1%
 
114010.1%
 
114410.1%
 
123210.1%
 
124510.1%
 
ValueCountFrequency (%) 
4671100010.1%
 
4639000010.1%
 
4626300010.1%
 
4617000010.1%
 
4607563410.1%
 

producerprice_pigs_live_slcpertonne
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct601
Distinct (%)93.3%
Missing1136
Missing (%)63.8%
Infinite0
Infinite (%)0.0%
Mean812224.1506
Minimum829
Maximum34651034
Zeros0
Zeros (%)0.0%
Memory size13.9 KiB
2022-04-08T09:39:51.534626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum829
5-th percentile1120.75
Q12351.5
median13228
Q3125423.75
95-th percentile2209493.8
Maximum34651034
Range34650205
Interquartile range (IQR)123072.25

Descriptive statistics

Standard deviation3992165.526
Coefficient of variation (CV)4.915103205
Kurtosis46.45464012
Mean812224.1506
Median Absolute Deviation (MAD)12046.5
Skewness6.765222472
Sum523072353
Variance1.593738559e+13
MonotocityNot monotonic
2022-04-08T09:39:51.712985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13228120.7%
 
680170.4%
 
1100040.2%
 
248640.2%
 
504040.2%
 
192857130.2%
 
121230.2%
 
103420.1%
 
807520.1%
 
641820.1%
 
Other values (591)60133.8%
 
(Missing)113663.8%
 
ValueCountFrequency (%) 
82910.1%
 
93610.1%
 
95710.1%
 
98710.1%
 
101310.1%
 
ValueCountFrequency (%) 
3465103410.1%
 
3268891610.1%
 
3152979310.1%
 
3075738620.1%
 
3010000010.1%
 

producerprice_pigs_carcass_usdpertonne
Real number (ℝ≥0)

MISSING

Distinct381
Distinct (%)93.6%
Missing1373
Missing (%)77.1%
Infinite0
Infinite (%)0.0%
Mean2822.614251
Minimum1236
Maximum6957
Zeros0
Zeros (%)0.0%
Memory size13.9 KiB
2022-04-08T09:39:51.898009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1236
5-th percentile1534.6
Q11982.5
median2454
Q33280.5
95-th percentile5673.2
Maximum6957
Range5721
Interquartile range (IQR)1298

Descriptive statistics

Standard deviation1226.677467
Coefficient of variation (CV)0.4345891283
Kurtosis1.438180284
Mean2822.614251
Median Absolute Deviation (MAD)579
Skewness1.408815714
Sum1148804
Variance1504737.607
MonotocityNot monotonic
2022-04-08T09:39:52.067367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
254440.2%
 
652640.2%
 
316030.2%
 
653230.2%
 
183520.1%
 
195220.1%
 
166920.1%
 
309120.1%
 
170720.1%
 
278520.1%
 
Other values (371)38121.4%
 
(Missing)137377.1%
 
ValueCountFrequency (%) 
123610.1%
 
126110.1%
 
126310.1%
 
133610.1%
 
135110.1%
 
ValueCountFrequency (%) 
695710.1%
 
684010.1%
 
653230.2%
 
652640.2%
 
646110.1%
 

producerprice_pigs_live_usdpertonne
Real number (ℝ≥0)

MISSING

Distinct542
Distinct (%)85.1%
Missing1143
Missing (%)64.2%
Infinite0
Infinite (%)0.0%
Mean2060.464678
Minimum414
Maximum7174
Zeros0
Zeros (%)0.0%
Memory size13.9 KiB
2022-04-08T09:39:52.252265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum414
5-th percentile959.2
Q11382
median1730
Q32451
95-th percentile4535.8
Maximum7174
Range6760
Interquartile range (IQR)1069

Descriptive statistics

Standard deviation1054.519757
Coefficient of variation (CV)0.51178735
Kurtosis3.403649037
Mean2060.464678
Median Absolute Deviation (MAD)460
Skewness1.690291495
Sum1312516
Variance1112011.919
MonotocityNot monotonic
2022-04-08T09:39:52.415072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4899120.7%
 
136140.2%
 
248640.2%
 
407440.2%
 
184930.2%
 
114330.2%
 
172130.2%
 
136330.2%
 
131720.1%
 
169120.1%
 
Other values (532)59733.5%
 
(Missing)114364.2%
 
ValueCountFrequency (%) 
41410.1%
 
46310.1%
 
47310.1%
 
50710.1%
 
54410.1%
 
ValueCountFrequency (%) 
717410.1%
 
688110.1%
 
680410.1%
 
632210.1%
 
553710.1%
 

producerprice_pigs_carcass_index
Real number (ℝ≥0)

MISSING

Distinct114
Distinct (%)9.8%
Missing619
Missing (%)34.8%
Infinite0
Infinite (%)0.0%
Mean102.464255
Minimum29
Maximum370
Zeros0
Zeros (%)0.0%
Memory size13.9 KiB
2022-04-08T09:39:52.637722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile75
Q193
median100
Q3109
95-th percentile132
Maximum370
Range341
Interquartile range (IQR)16

Descriptive statistics

Standard deviation23.13451606
Coefficient of variation (CV)0.2257813329
Kurtosis46.91687997
Mean102.464255
Median Absolute Deviation (MAD)8
Skewness4.759520432
Sum118961
Variance535.2058333
MonotocityNot monotonic
2022-04-08T09:39:52.816112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
100583.3%
 
98452.5%
 
99442.5%
 
101422.4%
 
95412.3%
 
94412.3%
 
96372.1%
 
106372.1%
 
104362.0%
 
108352.0%
 
Other values (104)74541.9%
 
(Missing)61934.8%
 
ValueCountFrequency (%) 
2910.1%
 
3110.1%
 
3210.1%
 
3620.1%
 
4810.1%
 
ValueCountFrequency (%) 
37010.1%
 
36010.1%
 
34510.1%
 
32710.1%
 
19510.1%
 

producerprice_pigs_live_index
Real number (ℝ≥0)

MISSING

Distinct124
Distinct (%)10.5%
Missing601
Missing (%)33.8%
Infinite0
Infinite (%)0.0%
Mean102.8481764
Minimum10
Maximum522
Zeros0
Zeros (%)0.0%
Memory size13.9 KiB
2022-04-08T09:39:52.985440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile74
Q193
median100
Q3108
95-th percentile129.1
Maximum522
Range512
Interquartile range (IQR)15

Descriptive statistics

Standard deviation27.4047482
Coefficient of variation (CV)0.2664582801
Kurtosis63.74820782
Mean102.8481764
Median Absolute Deviation (MAD)8
Skewness5.586763725
Sum121258
Variance751.0202238
MonotocityNot monotonic
2022-04-08T09:39:53.154727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
100693.9%
 
105452.5%
 
101452.5%
 
98442.5%
 
99432.4%
 
97432.4%
 
94372.1%
 
96362.0%
 
102362.0%
 
104341.9%
 
Other values (114)74742.0%
 
(Missing)60133.8%
 
ValueCountFrequency (%) 
1010.1%
 
1720.1%
 
2720.1%
 
3510.1%
 
3620.1%
 
ValueCountFrequency (%) 
52210.1%
 
35610.1%
 
31010.1%
 
30610.1%
 
29810.1%
 

_merge_prodprice
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
both
1318 
left_only
462 
ValueCountFrequency (%) 
both131874.0%
 
left_only46226.0%
 
2022-04-08T09:39:53.317583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-08T09:39:53.403826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:53.486521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length4
Mean length5.297752809
Min length4

Interactions

2022-04-08T09:39:19.102177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:19.264958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:19.421674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:19.587511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:19.734720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:19.888502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:20.035564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:20.213544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:20.367444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:20.521185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:20.690465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:20.837695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:21.007039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:21.154226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:21.307887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:21.470705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:21.624452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:21.778107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:21.925342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:22.079038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:22.226238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:22.379971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:22.542825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:22.831060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:23.051415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:23.194672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:23.356338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:23.498608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:23.675591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:23.821454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:23.965123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:24.106808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:24.249124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:24.399599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:24.547201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:24.700945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:24.901460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:25.102012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:25.271359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:25.434171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:25.666020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:25.819759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:25.973428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:26.136241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:26.290064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:26.437174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:26.606526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:26.753679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:26.907455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:27.070216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:27.208316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:27.393213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:27.571599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:27.709688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:27.856837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:27.995012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:28.142172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:28.273690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:28.411828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:28.558931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:28.743849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:28.897546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:29.044717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:29.182806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:29.330054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:29.461408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:29.599454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:29.746595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:29.884647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:30.031801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2022-04-08T09:39:30.750186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:30.897681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:31.029217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:31.182871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:31.314460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:31.477045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:31.646323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:31.784321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:31.947171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:32.085215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:32.248037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:32.401615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:32.564397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:32.718055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:32.865289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:33.034578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:33.188328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:33.335489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:33.482552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:33.651953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:33.790037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:33.921623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:34.068767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:34.206889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:34.354059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:34.492047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:34.686032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:34.824101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:34.955649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:35.109420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:35.240948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:35.394550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:35.572996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:35.726708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:35.873815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:36.961505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:37.115204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:37.277989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:37.415987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:37.644049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:37.805770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:37.947464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:38.108107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:38.271728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:38.423430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:38.585046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:38.787399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:38.958748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:39.100529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:39.240884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:39.384587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:39.536126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:39.698064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:39.848588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:39.992651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:40.144244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:40.291320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:40.428545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:40.580093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:40.721817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:40.863491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:41.013290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:41.147593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:41.289216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:41.420763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:41.582352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:41.724655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:41.856097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:41.997552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:42.139059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:42.292164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:42.457082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:42.635446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:42.804702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:42.942858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:43.090021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:43.243750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:43.392511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:43.559810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:43.722661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:43.860745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:44.023380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:44.161504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:44.308582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:44.462400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:44.609508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:44.763184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:44.894728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:45.048016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:45.195243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:45.342273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:45.480180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:45.661451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:45.853267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:46.045753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-04-08T09:39:53.633630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-08T09:39:53.956412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-08T09:39:54.278094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-08T09:39:54.643217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-08T09:39:46.356026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:46.852360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:47.254031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:47.940205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

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0Albania2011668.013100.0196000.0163000.0False563000.0297488.0563000.0297488.05580.02948.0109.087.0both
1Albania2012663.013260.0200000.0158818.0False600000.0297411.0600000.0297411.05546.02749.0116.087.0both
2Albania2013663.013494.0203530.0152000.0FalseNaN302000.0NaN302000.0NaN2858.086.089.0both
3Albania2014579.011900.0205368.0172455.0FalseNaN329667.0NaN329667.0NaN3125.0103.097.0both
4Albania2015589.011424.0194029.0171400.0FalseNaN344754.0NaN344754.0NaN2737.098.0101.0both
5Albania2016570.011424.0200597.0181024.0FalseNaN346072.0NaN346072.0NaN2788.099.0102.0both
6Albania2017570.011561.0203002.0180087.0FalseNaN352731.0NaN352731.0NaN2962.0101.0104.0both
7Albania2018445.010317.0231680.0184133.0FalseNaN359129.0NaN359129.0NaN3326.0102.0106.0both
8Albania2019494.010232.0206967.0183847.0FalseNaN362279.0NaN362279.0NaN3298.097.0107.0both
9Albania2020494.09002.0182046.0158401.0FalseNaN385842.0NaN385842.0NaN3551.0NaNNaNboth

Last rows

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1770Zimbabwe2011551.019500.0354000.0396277.0FalseNaNNaNNaNNaNNaNNaNNaNNaNleft_only
1771Zimbabwe2012536.019400.0362000.0400000.0FalseNaNNaNNaNNaNNaNNaNNaNNaNleft_only
1772Zimbabwe2013551.020400.0370000.0415000.0FalseNaNNaNNaNNaNNaNNaNNaNNaNleft_only
1773Zimbabwe2014550.020800.0378000.0238145.0FalseNaNNaNNaNNaNNaNNaNNaNNaNboth
1774Zimbabwe2015549.021959.0400000.0345249.0FalseNaNNaNNaNNaNNaNNaNNaNNaNboth
1775Zimbabwe2016549.023091.0420577.0425540.0FalseNaNNaNNaNNaNNaNNaNNaNNaNboth
1776Zimbabwe2017549.011602.0211228.0242020.0FalseNaNNaNNaNNaNNaNNaNNaNNaNboth
1777Zimbabwe2018550.09562.0174010.0230424.0FalseNaNNaNNaNNaNNaNNaNNaNNaNboth
1778Zimbabwe2019550.010651.0193820.0279473.0FalseNaNNaNNaNNaNNaNNaNNaNNaNboth
1779Zimbabwe2020550.010107.0183923.0272206.0FalseNaNNaNNaNNaNNaNNaNNaNNaNleft_only